
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
      import textwrap

      from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      _lowerCamelCase     =False


_lowerCamelCase     =logging.get_logger(__name__)
_lowerCamelCase     ="ybelkada/fonts"





def        _a   (		):
 if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
  raise ImportError(
      F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
      """Pix2StructImageProcessor. Please upgrade torch."""				)





def        _a   (		lowerCamelCase,      lowerCamelCase,      lowerCamelCase				):
 requires_backends(lowerCamelCase_,      ["""torch"""]				)
 _check_torch_version()

 lowerCamelCase						:      List[Any]							     = image_tensor.unsqueeze(0				)
 lowerCamelCase						:      Optional[int]							     = torch.nn.functional.unfold(lowerCamelCase_,      (patch_height, patch_width),      stride=(patch_height, patch_width)				)
 lowerCamelCase						:      Union[str, Any]							     = patches.reshape(image_tensor.size(0				),      image_tensor.size(1				),      lowerCamelCase_,      lowerCamelCase_,      -1				)
 lowerCamelCase						:      Optional[int]							     = patches.permute(0,      4,      2,      3,      1				).reshape(
     image_tensor.size(2				) // patch_height,      image_tensor.size(3				) // patch_width,      image_tensor.size(1				) * patch_height * patch_width,      )
 return patches.unsqueeze(0				)





def        _a   (		lowerCamelCase,      lowerCamelCase = 36,      lowerCamelCase = "black",      lowerCamelCase = "white",      lowerCamelCase = 5,      lowerCamelCase = 5,      lowerCamelCase = 5,      lowerCamelCase = 5,      lowerCamelCase = None,      lowerCamelCase = None,      ):
 requires_backends(lowerCamelCase_,      """vision"""				)
 # Add new lines so that each line is no more than 80 characters.

 lowerCamelCase						:      List[Any]							     = textwrap.TextWrapper(width=80				)
 lowerCamelCase						:      str							     = wrapper.wrap(text=lowerCamelCase_				)
 lowerCamelCase						:      Tuple							     = '''\n'''.join(lowerCamelCase_				)

 if font_bytes is not None and font_path is None:
  lowerCamelCase						:      Any							     = io.BytesIO(lowerCamelCase_				)
 elif font_path is not None:
  lowerCamelCase						:      Tuple							     = font_path
 else:
  lowerCamelCase						:      List[str]							     = hf_hub_download(lowerCamelCase_,      """Arial.TTF"""				)
 lowerCamelCase						:      Optional[Any]							     = ImageFont.truetype(lowerCamelCase_,      encoding="""UTF-8""",      size=lowerCamelCase_				)

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 lowerCamelCase						:      List[str]							     = ImageDraw.Draw(Image.new("""RGB""",      (1, 1),      lowerCamelCase_				)				)
 lowerCamelCase						:      Any							     = temp_draw.textbbox((0, 0),      lowerCamelCase_,      lowerCamelCase_				)

 # Create the actual image with a bit of padding around the text.
 lowerCamelCase						:      Union[str, Any]							     = text_width + left_padding + right_padding
 lowerCamelCase						:      List[Any]							     = text_height + top_padding + bottom_padding
 lowerCamelCase						:      List[Any]							     = Image.new("""RGB""",      (image_width, image_height),      lowerCamelCase_				)
 lowerCamelCase						:      int							     = ImageDraw.Draw(lowerCamelCase_				)
 draw.text(xy=(left_padding, top_padding),      text=lowerCamelCase_,      fill=lowerCamelCase_,      font=lowerCamelCase_				)
 return image





def        _a   (		lowerCamelCase,      lowerCamelCase,      **lowerCamelCase				):
 requires_backends(lowerCamelCase_,      """vision"""				)

 # Convert to PIL image if necessary
 lowerCamelCase						:      str							     = to_pil_image(lowerCamelCase_				)

 lowerCamelCase						:      Dict							     = render_text(lowerCamelCase_,      **lowerCamelCase_				)
 lowerCamelCase						:      Tuple							     = max(header_image.width,      image.width				)

 lowerCamelCase						:      Optional[Any]							     = int(image.height * (new_width / image.width)				)
 lowerCamelCase						:      Dict							     = int(header_image.height * (new_width / header_image.width)				)

 lowerCamelCase						:      List[str]							     = Image.new("""RGB""",      (new_width, new_height + new_header_height),      """white"""				)
 new_image.paste(header_image.resize((new_width, new_header_height)				),      (0, 0)				)
 new_image.paste(image.resize((new_width, new_height)				),      (0, new_header_height)				)

 # Convert back to the original framework if necessary
 lowerCamelCase						:      Tuple							     = to_numpy_array(lowerCamelCase_				)

 if infer_channel_dimension_format(lowerCamelCase_				) == ChannelDimension.LAST:
  lowerCamelCase						:      Optional[int]							     = to_channel_dimension_format(lowerCamelCase_,      ChannelDimension.LAST				)

 return new_image





class 				A__						(      _SCREAMING_SNAKE_CASE):
      _UpperCAmelCase							:       Union[str, Any]       			=     ['''flattened_patches''']
      def __init__( self       ,    __magic_name__ = True       ,    __magic_name__ = True       ,    __magic_name__ = None       ,    __magic_name__ = 2_0_4_8       ,    __magic_name__ = False       ,    **__magic_name__       ,    ):
       super().__init__(**__UpperCAmelCase )
       lowerCamelCase						:      Optional[Any]							     = patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
       lowerCamelCase						:      List[Any]							     = do_normalize
       lowerCamelCase						:      int							     = do_convert_rgb
       lowerCamelCase						:      Dict							     = max_patches
       lowerCamelCase						:      List[str]							     = is_vqa
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    **__magic_name__ ):
       requires_backends(self.extract_flattened_patches       ,    """torch""" )
       _check_torch_version()

       # convert to torch
       lowerCamelCase						:      str							     = to_channel_dimension_format(__UpperCAmelCase       ,    ChannelDimension.FIRST )
       lowerCamelCase						:      int							     = torch.from_numpy(__UpperCAmelCase )

       lowerCamelCase						:      Tuple							     = patch_size['''height'''], patch_size['''width''']
       lowerCamelCase						:      Optional[int]							     = get_image_size(__UpperCAmelCase )

       # maximize scale s.t.
       lowerCamelCase						:      List[Any]							     = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
       lowerCamelCase						:      Optional[int]							     = max(min(math.floor(scale * image_height / patch_height )       ,    __UpperCAmelCase )       ,    1 )
       lowerCamelCase						:      List[Any]							     = max(min(math.floor(scale * image_width / patch_width )       ,    __UpperCAmelCase )       ,    1 )
       lowerCamelCase						:      Dict							     = max(num_feasible_rows * patch_height       ,    1 )
       lowerCamelCase						:      List[Any]							     = max(num_feasible_cols * patch_width       ,    1 )

       lowerCamelCase						:      List[str]							     = torch.nn.functional.interpolate(
           image.unsqueeze(0 )       ,    size=(resized_height, resized_width)       ,    mode="""bilinear"""       ,    align_corners=__UpperCAmelCase       ,    antialias=__UpperCAmelCase       ,    ).squeeze(0 )

       # [1, rows, columns, patch_height * patch_width * image_channels]
       lowerCamelCase						:      List[str]							     = torch_extract_patches(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

       lowerCamelCase						:      Tuple							     = patches.shape
       lowerCamelCase						:      Optional[Any]							     = patches_shape[1]
       lowerCamelCase						:      List[Any]							     = patches_shape[2]
       lowerCamelCase						:      Dict							     = patches_shape[3]

       # [rows * columns, patch_height * patch_width * image_channels]
       lowerCamelCase						:      Optional[Any]							     = patches.reshape([rows * columns, depth] )

       # [rows * columns, 1]
       lowerCamelCase						:      Union[str, Any]							     = torch.arange(__UpperCAmelCase ).reshape([rows, 1] ).repeat(1       ,    __UpperCAmelCase ).reshape([rows * columns, 1] )
       lowerCamelCase						:      str							     = torch.arange(__UpperCAmelCase ).reshape([1, columns] ).repeat(__UpperCAmelCase       ,    1 ).reshape([rows * columns, 1] )

       # Offset by 1 so the ids do not contain zeros, which represent padding.
       row_ids += 1
       col_ids += 1

       # Prepare additional patch features.
       # [rows * columns, 1]
       lowerCamelCase						:      str							     = row_ids.to(torch.floataa )
       lowerCamelCase						:      List[str]							     = col_ids.to(torch.floataa )

       # [rows * columns, 2 + patch_height * patch_width * image_channels]
       lowerCamelCase						:      str							     = torch.cat([row_ids, col_ids, patches]       ,    -1 )

       # [max_patches, 2 + patch_height * patch_width * image_channels]
       lowerCamelCase						:      Tuple							     = torch.nn.functional.pad(__UpperCAmelCase       ,    [0, 0, 0, max_patches - (rows * columns)] ).float()

       lowerCamelCase						:      str							     = to_numpy_array(__UpperCAmelCase )

       return result
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__ = None       ,    **__magic_name__ ):
       if image.dtype == np.uinta:
        lowerCamelCase						:      str							     = image.astype(np.floataa )

       # take mean across the whole `image`
       lowerCamelCase						:      str							     = np.mean(__UpperCAmelCase )
       lowerCamelCase						:      List[Any]							     = np.std(__UpperCAmelCase )
       lowerCamelCase						:      Optional[Any]							     = max(__UpperCAmelCase       ,    1.0 / math.sqrt(np.prod(image.shape ) ) )

       return normalize(__UpperCAmelCase       ,    mean=__UpperCAmelCase       ,    std=__UpperCAmelCase       ,    **__UpperCAmelCase )






      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__ = None       ,    __magic_name__ = None       ,    __magic_name__ = None       ,    __magic_name__ = None       ,    __magic_name__ = None       ,    __magic_name__ = None       ,    __magic_name__ = ChannelDimension.FIRST       ,    **__magic_name__       ,    ):
       lowerCamelCase						:      Union[str, Any]							     = do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase						:      Union[str, Any]							     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       lowerCamelCase						:      Tuple							     = patch_size if patch_size is not None else self.patch_size
       lowerCamelCase						:      Optional[Any]							     = max_patches if max_patches is not None else self.max_patches
       lowerCamelCase						:      List[Any]							     = self.is_vqa

       if kwargs.get("""data_format"""       ,    __UpperCAmelCase ) is not None:
        raise ValueError("""data_format is not an accepted input as the outputs are """ )

       lowerCamelCase						:      int							     = make_list_of_images(__UpperCAmelCase )

       if not valid_images(__UpperCAmelCase ):
        raise ValueError(
            """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
            """torch.Tensor, tf.Tensor or jax.ndarray.""" )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
        lowerCamelCase						:      Union[str, Any]							     = [convert_to_rgb(__UpperCAmelCase ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase						:      Dict							     = [to_numpy_array(__UpperCAmelCase ) for image in images]

       if is_vqa:
        if header_text is None:
         raise ValueError("""A header text must be provided for VQA models.""" )
        lowerCamelCase						:      Any							     = kwargs.pop("""font_bytes"""       ,    __UpperCAmelCase )
        lowerCamelCase						:      Dict							     = kwargs.pop("""font_path"""       ,    __UpperCAmelCase )

        if isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
         lowerCamelCase						:      Optional[Any]							     = [header_text] * len(__UpperCAmelCase )

        lowerCamelCase						:      Optional[Any]							     = [
            render_header(__UpperCAmelCase       ,    header_text[i]       ,    font_bytes=__UpperCAmelCase       ,    font_path=__UpperCAmelCase )
            for i, image in enumerate(__UpperCAmelCase )
        ]

       if do_normalize:
        lowerCamelCase						:      Any							     = [self.normalize(image=__UpperCAmelCase ) for image in images]

       # convert to torch tensor and permute
       lowerCamelCase						:      int							     = [
           self.extract_flattened_patches(image=__UpperCAmelCase       ,    max_patches=__UpperCAmelCase       ,    patch_size=__UpperCAmelCase )
           for image in images
       ]

       # create attention mask in numpy
       lowerCamelCase						:      Union[str, Any]							     = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]

       lowerCamelCase						:      int							     = BatchFeature(
           data={"""flattened_patches""": images, """attention_mask""": attention_masks}       ,    tensor_type=__UpperCAmelCase )

       return encoded_outputs







import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






